Optical navigation upon arbitrary surfaces produces motion signals indicative of relative movement along the directions of coordinate axes, and is becoming increasingly prevalent. It is used, for instance, in optical computer mice and fingertip tracking devices to replace conventional mice and trackballs for the position control of screen pointers in windowed user interfaces for computer systems. It has many advantages, among which are the lack of moving parts that accumulate dirt and suffer the mechanical wear and tear of use. Another advantage of an optical mouse is that it does not need a mouse pad, since it is generally capable of navigating upon arbitrary surfaces, so long as they are not optically featureless.
Optical navigation operates by tracking the relative displacement of images. A two-dimensional view of a portion of a surface is focused upon an array of photodetectors, whose outputs are digitized and stored as a reference image in a corresponding array of memory. A brief time later a sample image is also digitized. If there has been no motion between the image capture events, then the sample image and the reference image are identical (or very nearly so). That is, the stored arrays appear to match up. If, on the other hand, there has been some motion between the image capture events, then the sample image will appear to have shifted within its borders, and the digitized arrays will no longer match. The matching process that is used to align similar features of two images is termed “correlation” and typically involves a two-dimensional cross-correlation between the reference image and the sample image. A two-dimensional cross-correlation between the reference image and the sample image compares intensity values of the images on a pixel-by-pixel basis to determine relative displacement between the two sets of image data.
Because two-dimensional cross-correlation is performed on a pixel-by-pixel basis, it requires a large number of arithmetic computations to compare all of the data points of the two sets of image data. For example, a 30 by 30 photodetector array has 900 pixels (i.e., individual photodetectors), which produce 900 different data points that must be cross-correlated between the two sets of image data. Generally, the larger the number of arithmetic computations that are required to perform the cross-correlation, the more time is required to determine the relative displacement. The required processing time can be reduced by adding more processing power. However, more processing power requires more space on a processing integrated circuit (IC) and consumes more power during operation, both of which are costly resources that are to be conserved whenever possible.
In view of this, what is needed is a low-cost and accurate technique for optical navigation.